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sfHMM

sfHMM (Step-Finding based Hidden Markov Model analysis) provides efficient HMM analysis for varieties of biophysical data. Owing to its accurate parameter estimation workflows, sfHMM can be applied to systems with:

  • unknown numbers of states
  • high noise
  • sparse transition

ex1

You can also use sfHMM just for step finding or AIC/BIC based Gaussian mixture model selection. Please refer to "example.ipynb" for basic usages.

Installation

  • pip installation

Working in a new environment is highly recommended. For example, if you are using Anaconda:

conda create -n myenv python
conda activate myenv

then it's ready to install sfHMM.

pip install git+https://github.com/hanjinliu/sfHMM
  • cloning from the source

If you want to clone from the source, you need Rust installed in your local environment.

git clone https://github.com/hanjinliu/sfHMM
cd sfHMM
pip install -e .

Contents

sfHMM

This module contains basic classes and functions for sfHMM data analysis.

  • sfHMM1 ... sfHMM for single trajectory.
  • sfHMMn ... sfHMM for multiple trajectories.
  • hmm_sampling ... sample data generation of smFRET-like data.
  • motor_sampling ... sample data generation of motor-like data.

sfHMM.motor

This module contains sfHMM classes aiming at analyzing motor-stepping or other sparse transition models.

  • sfHMM1Motor ... sfHMM for single motor-stepping trajectory.
  • sfHMMnMotor ... sfHMM for multiple motor-stepping trajectories.

sfHMM.step

This module contains several step finding algorithms in the same API. My efficient implementation enables analysis of 100,000 data points within ~1 sec!

  • GaussStep ... step finding for Gauss distribution [1].
  • PoissonStep ... step finding for Poisson distribution.
  • SDFixedGaussStep ... step finding for Gauss distribution with fixed standard deviation.
  • TtestStep ... step finding using T-test [2].
  • BayesianPoissonStep ... step finding in Bayesian method [3].

sfHMM.gmm

This module contains classes that inherit sklearn.mixture.GaussianMixture while making Gaussian mixture model clustering much easier.

  • GMMs ... Fitting Gaussian mixtures and model selection.

sfHMM.io

This module contains input/output functions that are suitable for sfHMM data analysis or other one-dimensional signal processing.

  • read ... Not recommended now. Use "read" method in sfHMM classes instead
  • read_excel ... Load all the sheets from Excel files.
  • save ... Not recommended now. Use "save" method in sfHMM classes instead

sfHMM.viewer

This module contains interactive multi-channel viewer for 1-D data visualization (work in progress).

  • TrajectoryViewer ... Qt viewer object.

Common Parameters

All the parameters are optional.

  • sg0 ... The parameter used in denoising process.
  • psf ... The parameter used in step finding.
  • krange ... Range of the number of hidden states to test.
  • model ... Distribution of signal. Gaussian and Poissonian are supported now.
  • name ... Name of the object.

Common Methods

  • run_all() ... Run all the needed algorithm in the most plausible condition.
  • step_finding() ... Step finding by likelihood maximization.
  • denoising() ... The standard deviation of noise is cut off to sg0.
  • gmmfit() ... Gaussian mixture model clustering.
  • hmmfit() ... HMM parameter initialization and optimization.
  • plot() ... Visualize the results of sfHMM analysis.
  • tdp() ... Show the results in pseudo transition density plot.
  • read() ... Load such as csv, txt, dat files, or the first sheet of Excel files.
  • save() ... Save sfHMM analysis results.

Use sfHMM in GUI

Since v0.8, sfHMM is now available as a himena plugin, which means that you can run sfHMM algorithms along with the powerful GUI and any other himena plugins.

himena

You can install himena and sfHMM plugin as follows:

pip install himena[recommended]
himena --install sfHMM

and start himena application:

himena

Now, you can create sfHMM window from Tools > sfHMM menu.

Citation

If you found sfHMM useful, please consider citing our paper.

A fast and objective hidden Markov modeling for accurate analysis of biophysical data with numerous states
Hanjin Liu, Tomohiro Shima
bioRxiv 2021.05.30.446337; doi: https://doi.org/10.1101/2021.05.30.446337

References

[1] Kalafut, B., & Visscher, K. (2008). An objective, model-independent method for detection of non-uniform steps in noisy signals. Computer Physics Communications, 179(10), 716-723.

[2] Shuang, B., Cooper, D., Taylor, J. N., Kisley, L., Chen, J., Wang, W., ... & Landes, C. F. (2014). Fast step transition and state identification (STaSI) for discrete single-molecule data analysis. The journal of physical chemistry letters, 5(18), 3157-3161.

[3] Ensign, D. L., & Pande, V. S. (2010). Bayesian detection of intensity changes in single molecule and molecular dynamics trajectories. Journal of Physical Chemistry B, 114(1), 280–292.

Metadata

Release files for sfhmm 0.8.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for sfhmm 0.8.0
File Size Uploaded Provenance
sfHMM-0.8.0.tar.gz 8.3 MB Without provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for sfhmm 0.8.0
File
sfHMM-0.8.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.17+ x86-64 Details
sfHMM-0.8.0-pp311-pypy311_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-pp311-pypy311_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-pp311-pypy311_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.5+ x86-32 Details
sfHMM-0.8.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl PyPy 3.10 PyPy 3.10 7.3 Linux glibc 2.17+ x86-64 Details
sfHMM-0.8.0-pp310-pypy310_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl PyPy 3.10 PyPy 3.10 7.3 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-pp310-pypy310_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl PyPy 3.10 PyPy 3.10 7.3 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-pp310-pypy310_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl PyPy 3.10 PyPy 3.10 7.3 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl PyPy 3.10 PyPy 3.10 7.3 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl PyPy 3.10 PyPy 3.10 7.3 Linux glibc 2.5+ x86-32 Details
sfHMM-0.8.0-pp39-pypy39_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl PyPy 3.9 PyPy 3.9 7.3 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-pp39-pypy39_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl PyPy 3.9 PyPy 3.9 7.3 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-pp39-pypy39_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl PyPy 3.9 PyPy 3.9 7.3 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl PyPy 3.9 PyPy 3.9 7.3 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl CPython 3.13 CPython 3.13 free-threading Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl CPython 3.13 CPython 3.13 free-threading Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl CPython 3.13 CPython 3.13 free-threading Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 free-threading Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
sfHMM-0.8.0-cp313-cp313-win32.whl CPython 3.13 CPython 3.13 Windows x86-32 Details
sfHMM-0.8.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64 Details
sfHMM-0.8.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl CPython 3.13 CPython 3.13 Linux glibc 2.5+ x86-32 Details
sfHMM-0.8.0-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
sfHMM-0.8.0-cp313-cp313-macosx_10_12_x86_64.whl CPython 3.13 CPython 3.13 macOS 10.12+ x86-64 Details
sfHMM-0.8.0-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
sfHMM-0.8.0-cp312-cp312-win32.whl CPython 3.12 CPython 3.12 Windows x86-32 Details
sfHMM-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64 Details
sfHMM-0.8.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl CPython 3.12 CPython 3.12 Linux glibc 2.5+ x86-32 Details
sfHMM-0.8.0-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
sfHMM-0.8.0-cp312-cp312-macosx_10_12_x86_64.whl CPython 3.12 CPython 3.12 macOS 10.12+ x86-64 Details
sfHMM-0.8.0-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
sfHMM-0.8.0-cp311-cp311-win32.whl CPython 3.11 CPython 3.11 Windows x86-32 Details
sfHMM-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64 Details
sfHMM-0.8.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl CPython 3.11 CPython 3.11 Linux glibc 2.5+ x86-32 Details
sfHMM-0.8.0-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
sfHMM-0.8.0-cp311-cp311-macosx_10_12_x86_64.whl CPython 3.11 CPython 3.11 macOS 10.12+ x86-64 Details
sfHMM-0.8.0-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
sfHMM-0.8.0-cp310-cp310-win32.whl CPython 3.10 CPython 3.10 Windows x86-32 Details
sfHMM-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ x86-64 Details
sfHMM-0.8.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl CPython 3.10 CPython 3.10 Linux glibc 2.5+ x86-32 Details
sfHMM-0.8.0-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
sfHMM-0.8.0-cp310-cp310-macosx_10_12_x86_64.whl CPython 3.10 CPython 3.10 macOS 10.12+ x86-64 Details
sfHMM-0.8.0-cp39-cp39-win_amd64.whl CPython 3.9 CPython 3.9 Windows x86-64 Details
sfHMM-0.8.0-cp39-cp39-win32.whl CPython 3.9 CPython 3.9 Windows x86-32 Details
sfHMM-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-64 Details
sfHMM-0.8.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ IBM System/390x Details
sfHMM-0.8.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ PowerPC 64-le Details
sfHMM-0.8.0-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ ARMv7l Details
sfHMM-0.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ ARM64 Details
sfHMM-0.8.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl CPython 3.9 CPython 3.9 Linux glibc 2.5+ x86-32 Details

Total release size: 30.8 MB

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